Abstract
Ant Colony Optimization is a bio-inspired technique that can be applied to solve hard optimization problems. A key issue is how to design the communication mechanism between ants that allows them to effectively solve a problem. We propose a novel approach to this issue by evolving the current pheromone trail update methods. Results obtained with the TSP show that the evolved strategies perform well and exhibit a good generalization capability when applied to larger instances.
Keywords
- Particle Swarm Optimization
- Travel Salesman Problem
- Pheromone Trail
- Genetic Program Algorithm
- Good Generalization Capability
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming. Published via and freely (With contributions by J. R. Koza) (2008), http://lulu.com , http://www.gp-field-guide.org.uk
Diosan, L., Oltean, M.: Evolutionary design of evolutionary algorithms. Genetic Programming and Evolvable Machines 10, 263–306 (2009)
Botee, H., Bonabeau, E.: Evolving ant colony optimization. Advances in Complex Systems 1, 149–159 (1998)
White, T., Pagurek, B., Oppacher, F.: ASGA: Improving the ant system by integration with genetic algorithms. In: Proc. of the Third Genetic Programming Conference, pp. 610–617. Morgan Kaufmann, San Francisco (1998)
Poli, R., Langdon, W.B., Holland, O.: Extending particle swarm optimisation via genetic programming. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 291–300. Springer, Heidelberg (2005)
Diosan, L., Oltean, M.: Evolving the structure of the particle swarm optimization algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 25–36. Springer, Heidelberg (2006)
Runka, A.: Evolving an edge selection formula for ant colony optimization. In: GECCO 2009 Proceedings, pp. 1075–1082 (2009)
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Tavares, J., Pereira, F.B. (2010). Evolving Strategies for Updating Pheromone Trails: A Case Study with the TSP. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_53
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DOI: https://doi.org/10.1007/978-3-642-15871-1_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
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